What are the differences between Spark and pandas DataFrames?
Apache Spark DataFrames constitute a distributed assembly of data arranged into columns with specific names. On the other hand, pandas DataFrames embodies a two-dimensional tabular arrangement of data featuring labeled axes for both rows and columns. Conceptually, it can be likened to a spreadsheet or an SQL table, offering an organized representation of data with rows and columns.
Key differences
Apache Spark and pandas are widely utilized tools for data processing and analysis, leveraging DataFrames for effective data manipulation. Nonetheless, substantial distinctions exist between Spark DataFrames and pandas DataFrames, encompassing differences in their foundational architecture, performance, scalability, and utilization. Some of the key differences are mentioned below:
pandas | Spark | |
Architecture | pandas is a Python library that operates in memory on a single machine. It stores data in RAM, limiting the amount of data that can be processed to the available memory on the machine. | Apache Spark is a distributed processing framework operating on a machine cluster. It can handle much larger datasets by distributing the workload across multiple nodes in the cluster. |
Performance and Scalability | pandas is optimized for in-memory operations on a single machine, making it efficient for smaller datasets that can fit into memory. However, it struggles with performance and efficiency when handling large datasets. | Spark is specifically crafted for distributed processing, allowing it to effectively manage extensive data processing by harnessing a cluster’s parallel capabilities. It excels at handling and analyzing enormous datasets that surpass a single machine’s memory capacity. |
Lazy Evaluation | pandas is based on an eager evaluation approach, where operations are promptly executed and results are instantly provided. | Spark employs a lazy evaluation strategy, deferring the execution of transformations on DataFrames until an action is initiated. This approach enables Spark to optimize the entire computational plan before operations. |
Data Loading and Saving | pandas offers seamless data loading into a DataFrame from diverse file formats such as CSV, Excel, and SQL using its integrated functions. Saving data to different file formats is also straightforward with pandas. | Spark can read from and write to various data sources, including distributed file systems like HDFS, databases, and cloud storage options such as Amazon S3 and Azure Blob Storage. |
Data Processing and Transformations | pandas offers an extensive array of functions for data manipulation, facilitating effortless data transformation and analysis through diverse methods and operations. | Spark offers a comparable set of functions for data manipulation and transformation as pandas but operates on a distributed scale, allowing for parallel processing across the cluster. |
Use Cases | pandas is ideal for conducting exploratory data analysis, handling small to medium-sized datasets, and preprocessing tasks that can be accommodated within memory. | Spark is well-suited for processing large volumes of data, distributed machine learning, real-time analytics, and managing massive datasets that exceed the memory capacity of a single machine. |
Code example
The working example below highlights the differences in defining and performing operations on data frames in pandas and PySpark:
# Importing librariesimport pandas as pdfrom pyspark.sql import SparkSessionfrom pyspark.sql.types import StructType, StructField, StringType, IntegerType# Creating the sample pandas DataFrameprint("Pandas DataFrame Example:")sample_data_pandas = {'Team': ['Arsenal', 'Real Madrid', 'Bayern Munich', 'PSG'],'Points': [30, 65, 45, 42]}pandas_example = pd.DataFrame(sample_data_pandas)# Printing the pandas DataFrameprint("Pandas DataFrame:")print(pandas_example)# Simple operation to add three points to each team’s totalpandas_example['Points'] = pandas_example['Points'] + 3# Printing the updated pandas DataFrameprint("\nUpdated Pandas DataFrame:")print(pandas_example)print("\nSpark DataFrame Example:")# Initializing a Spark sessionspark = SparkSession.builder \.appName('Spark DataFrame Example') \.getOrCreate()# Creating an identical sample DataFrame using Sparklayout = StructType([StructField('Name', StringType(), True),StructField('Points', IntegerType(), True)])# Populating the Spark DataFramesample_data_spark = [('Arsenal', 30), ('Real Madrid', 65), ('Bayern Munich', 45), ('PSG', 42)]spark_example = spark.createDataFrame(sample_data_spark, schema=layout)# Printing the Spark DataFrameprint("Spark DataFrame:")spark_example.show()# Simple operation to add three points to each team’s totalspark_example = spark_example.withColumn('Points', spark_example['Points'] + 3)# Printing the updated Spark DataFrameprint("\nUpdated Spark DataFrame:")spark_example.show()# Stopping the Spark sessionspark.stop()
Explanation
Let’s discuss the code above.
Lines 8–11: We create a sample
pandasdataset.Lines 14–15: We print the sample
pandasdataset.Line 18: We perform a simple operation to add three points to the total of all the teams in our sample dataset.
Lines 21–22: We print the updated
pandasdataset after performing the operation.Lines 27–29: We create a
SparkSessionobject namedspark. We use thebuilderattribute to configure and set various options for theSparkSession. We useappName()to set the name of the Spark application to 'Spark DataFrame Example' and thegetOrCreate()method to either retrieve an existingSparkSessionor create a new one if none exists.Lines 32–35: We create the layout for the Spark dataset example.
Lines 37–38: We populate the Spark dataset.
Lines 41–42: We print the Spark dataset.
Line 45: We perform a similar operation in Spark to add three points to the total of all teams in the sample Spark dataset.
Lines 48–49: We print the updated Spark dataset after performing the operation.
Line 52: We stop the Spark session.
Deciding between using pandas and Spark
Some of the key considerations to keep in mind while choosing between the two DataFrames are listed below:
Factor | pandas | Spark |
The Size of Dataset | We employ pandas when dealing with small to medium-sized datasets that can easily be accommodated within the memory of a single machine. | We opt for Spark DataFrames when handling extensive datasets that surpass the memory limits of a single machine, as Spark can effectively distribute and process data across a cluster. |
Computational Complexity | When it comes to basic data manipulations and analysis on a singular machine, pandas DataFrames is generally more suitable and offers an extensive range of features. | Spark DataFrames might be a more fitting choice for complex computations, machine learning, graph processing, or distributed computing. |
Ease of Use | pandas is recognized for its easily usable API and is extensively utilized for exploring and analyzing data because of its straightforward syntax. | Using Spark DataFrames requires a more challenging learning process, and the setup may be more intricate, particularly in a distributed computing setting. |
Resource Availability | In the case of smaller datasets and environments with constrained resources, pandas is a more pragmatic selection. | If operational costs are not a problem, by utilizing a cluster of machines, Spark DataFrames can leverage distributed computing, offering scalability. |
Conclusion
In summary, pandas is tailored for processing data in-memory on a single machine, whereas Spark is crafted for distributed data processing across a cluster of machines, enabling efficient handling of large-scale data. The decision between the two depends on the dataset size and the scale of data processing requirements.
